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B0931
Title: Exact subsampling in MCMC using piecewise deterministic Markov processes Authors:  Joris Bierkens - Delft Institute of Applied Mathematics (Netherlands) [presenting]
Abstract: Markov chain Monte Carlo methods provide an essential tool in statistics for sampling from complex probability distributions. While the standard approach to MCMC involves constructing discrete-time reversible Markov chains whose transition kernel is obtained via e.g. the Metropolis-Hastings algorithm, there has been recent interest in alternative schemes based on piecewise deterministic Markov processes (PDMPs). One such approach is based on the Zig-Zag process, which proved to provide a highly scalable sampling scheme for sampling in the big data regime, as it allows for subsampling without modifying the posterior distribution. We will present a broad overview of these methods along with some theoretical results.